In radiotherapy or radiosurgery, treatment planning is generally performed using medical imaging of the patient. Analysis of such imaging generally involves delineation of target volumes and critical organs in the medical images. For example, segmentation or contouring of tumor and organs-at-risk (OARs) from patient images is generally considered a prerequisite for radiotherapy planning. Manual segmentation is generally tedious, time-consuming, and may suffer from large intra-rater and inter-rater variations. Fully automated segmentation of x-ray computed tomography (CT) or magnetic resonance (MR) images using generally-available techniques has been proven to be challenging, such as due to image noise or other artifacts. Such imaging generally provides only limited image contrast for most soft-tissue structures, in one approach, atlas-based auto-segmentation (ABAS) techniques may be used, and provide an ability to incorporate prior anatomical information about structure shapes and their geometric relationships. ABAS-based approaches may present challenges. For example, in ABAS, segmentation accuracy generally depends on atlas quality, and a computation time is generally proportional to a count of atlases used. In an example, a multi-atlas label fusion technique, may be used, but segmentation accuracy may be unsatisfactory for certain applications and manual editing of the results may still be performed.